[kmicinski]

I am the author of this paper, and I do not agree with Dr. Smaradgakis' comments. As far as I can tell, the root of his concern is that that paper did not target Souffle Datalog, a specific Datalog language in which his group writes. The criticism is totally fair in a sense, but I do not agree with you that these are "pretty big caveats" in our paper, for the reasons I address in my rebuttal to his comment. I will say however, that his very engaging comments have pushed us to do significant follow-on work, which has now pushed our engines to scale to the kind of code he writes in Datalog, yielding very exciting results, and I am hoping that he will be satisfies when he sees it :-)

I will also mention that our group has follow-on work from this (I cannot share this widely due to reviewing reasons but a preprint is available if you would like to search) which significantly addresses Yiannais' concerns. In the engine cited here, we scale to small programs (tens of lines): our engine does not support large, tricky queries for interesting, asymptotic reasons (which are also shared by other Datalog engines based upon binary joins, not unique to our engines). Our new engines port a significantly more complex class of join algorithms to the GPU, and we have used these new algorithms (and our novel GPU-based implementation) to run 500-1000-line Datalog programs which beat all existing state-of-the-art program analysis engines by 20-50x.

In sum, I strongly disagree with the "pretty big caveats" remark. Dr. Smaradgakis' comments are quite firm in nature and I very much respect them. But I encourage you to check out my rebuttal and also (regarding scaling to larger subsets of Datalog and "real" programs) our recent follow-on work.

If you would like proof, please email me, we are happy to help you evaluate for yourself. My email is always open: [e-mail].